mirror of
https://github.com/saymrwulf/pytorch.git
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a2802ad0b9
Updating nvfuser code base. This should fix the indexing issue observed in https://github.com/pytorch/vision/issues/6015. Running tests locally as well. Will update the description here at a later point @bypass-github-export-checks Pull Request resolved: https://github.com/pytorch/pytorch/pull/77471 Approved by: https://github.com/seemethere, https://github.com/eellison
339 lines
10 KiB
C++
339 lines
10 KiB
C++
#include <torch/csrc/jit/codegen/cuda/executor.h>
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#include <torch/csrc/jit/codegen/cuda/fusion.h>
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#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
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#include <torch/csrc/jit/codegen/cuda/ir_builder.h>
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#include <torch/csrc/jit/codegen/cuda/ir_utils.h>
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#include <torch/csrc/jit/codegen/cuda/lower2device.h>
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#include <torch/csrc/jit/codegen/cuda/ops/all_ops.h>
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#include <torch/csrc/jit/codegen/cuda/scheduler/all_schedulers.h>
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#include <benchmark/benchmark.h>
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#include <cuda_runtime.h>
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#include <benchmarks/cpp/nvfuser/utils.h>
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using namespace torch::jit::fuser::cuda;
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//------------------------------------------------------------------------------
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static void setupBatchNorm(Fusion* fusion, DataType dtype) {
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TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
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FusionGuard fg(fusion);
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const bool kTraining = true;
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const float kMomentum = 0.1;
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const float kEps = 1e-5;
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// setup fusion
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auto input = makeContigTensor(4, dtype);
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auto weight = makeContigTensor(1, dtype);
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auto bias = makeContigTensor(1, dtype);
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auto running_mean = makeContigTensor(1, DataType::Float);
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auto running_var = makeContigTensor(1, DataType::Float);
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fusion->addInput(input);
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fusion->addInput(weight);
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fusion->addInput(bias);
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fusion->addInput(running_mean);
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fusion->addInput(running_var);
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if (dtype == DataType::Half) {
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input = castOp(DataType::Float, input);
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weight = castOp(DataType::Float, weight);
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bias = castOp(DataType::Float, bias);
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}
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auto momentum_ptr = IrBuilder::create<Double>(kMomentum);
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auto eps_ptr = IrBuilder::create<Double>(kEps);
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auto result = batch_norm(
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input,
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weight,
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bias,
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running_mean,
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running_var,
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kTraining,
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momentum_ptr,
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eps_ptr);
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auto output = result.output;
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if (dtype == DataType::Half) {
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output = castOp(DataType::Half, output);
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}
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fusion->addOutput(output);
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}
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static void NvFuserScheduler_BatchNorm(
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benchmark::State& benchmark_state,
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FusionExecutorCache* fusion_executor_cache,
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DataType dtype) {
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TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
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const bool kTraining = true;
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const float kMomentum = 0.1;
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const float kEps = 1e-5;
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std::vector<int64_t> input_shape{
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benchmark_state.range(0),
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benchmark_state.range(1),
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benchmark_state.range(2),
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benchmark_state.range(2)};
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// inputs
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at::manual_seed(0);
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auto options =
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at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
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auto fp32_options =
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at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
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at::Tensor at_x = at::randn(input_shape, options);
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at::Tensor at_weight = at::ones({input_shape[1]}, options);
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at::Tensor at_bias = at::zeros({input_shape[1]}, options);
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at::Tensor at_run_mean = at::zeros({input_shape[1]}, fp32_options);
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at::Tensor at_run_var = at::ones({input_shape[1]}, fp32_options);
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std::vector<c10::IValue> aten_inputs(
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{at_x, at_weight, at_bias, at_run_mean, at_run_var});
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runBenchmarkIterations(benchmark_state, fusion_executor_cache, aten_inputs);
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benchmark_state.SetBytesProcessed(
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int64_t(benchmark_state.iterations()) *
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((2 * (at_x.numel() + at_weight.numel() + at_bias.numel())) *
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int64_t(dataTypeSize(dtype)) +
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(2 * (at_run_mean.numel() + at_run_var.numel()) *
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int64_t(dataTypeSize(DataType::Float)))));
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}
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//------------------------------------------------------------------------------
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static void Baseline_BatchNorm(
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benchmark::State& benchmark_state,
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DataType dtype) {
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TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
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const float kMomentum = 0.1;
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const float kEps = 1e-5;
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std::vector<int64_t> input_shape{
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benchmark_state.range(0),
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benchmark_state.range(1),
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benchmark_state.range(2),
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benchmark_state.range(2)};
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// inputs
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at::manual_seed(0);
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auto options =
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at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
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auto fp32_options =
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at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
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at::Tensor at_x = at::randn(input_shape, options);
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at::Tensor at_weight = at::ones({input_shape[1]}, options);
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at::Tensor at_bias = at::zeros({input_shape[1]}, options);
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at::Tensor at_run_mean = at::zeros({input_shape[1]}, fp32_options);
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at::Tensor at_run_var = at::ones({input_shape[1]}, fp32_options);
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auto ato_weight = c10::optional<at::Tensor>(at_weight);
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auto ato_bias = c10::optional<at::Tensor>(at_bias);
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auto ato_run_mean = c10::optional<at::Tensor>(at_run_mean);
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auto ato_run_var = c10::optional<at::Tensor>(at_run_var);
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auto output = at::batch_norm(
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at_x,
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ato_weight,
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ato_bias,
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ato_run_mean,
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ato_run_var,
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true,
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kMomentum,
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kEps,
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true);
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clearL2Cache();
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cudaDeviceSynchronize();
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for (auto _ : benchmark_state) {
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CudaKernelTimer timer;
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auto output = at::batch_norm(
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at_x,
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ato_weight,
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ato_bias,
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ato_run_mean,
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ato_run_var,
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true,
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kMomentum,
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kEps,
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true);
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benchmark_state.SetIterationTime(timer.elapsed() / 1000.0);
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cudaDeviceSynchronize();
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clearL2Cache();
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cudaDeviceSynchronize();
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}
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benchmark_state.SetBytesProcessed(
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int64_t(benchmark_state.iterations()) *
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((2 * (at_x.numel() + at_weight.numel() + at_bias.numel())) *
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int64_t(dataTypeSize(dtype)) +
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(2 * (at_run_mean.numel() + at_run_var.numel()) *
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int64_t(dataTypeSize(DataType::Float)))));
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}
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//------------------------------------------------------------------------------
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static void Baseline_BatchNorm_cuDNN_fp32(benchmark::State& benchmark_state) {
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Baseline_BatchNorm(benchmark_state, DataType::Float);
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}
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static void Baseline_BatchNorm_cuDNN_fp16(benchmark::State& benchmark_state) {
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Baseline_BatchNorm(benchmark_state, DataType::Half);
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}
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// Simple aliases just for names in the printed output
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static void Baseline_ResNet_BatchNorm_cuDNN_fp16(benchmark::State& benchmark_state) {
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Baseline_BatchNorm(benchmark_state, DataType::Half);
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}
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static void Baseline_ResNext_BatchNorm_cuDNN_fp16(benchmark::State& benchmark_state) {
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Baseline_BatchNorm(benchmark_state, DataType::Half);
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}
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//------------------------------------------------------------------------------
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NVFUSER_BENCHMARK_DEFINE(
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NvFuserScheduler_BatchNorm_fp32,
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setupBatchNorm,
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NvFuserScheduler_BatchNorm,
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DataType::Float);
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_BatchNorm_fp32)
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// ->RangeMultiplier(2)
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->Ranges({{64, 512}, {32, 128}, {2, 64}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_BatchNorm_fp32)
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// ->RangeMultiplier(2)
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->Ranges({{2, 64}, {2, 32}, {2, 256}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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NVFUSER_BENCHMARK_DEFINE(
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NvFuserScheduler_BatchNorm_fp16,
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setupBatchNorm,
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NvFuserScheduler_BatchNorm,
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DataType::Half);
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_BatchNorm_fp16)
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// ->RangeMultiplier(2)
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->Ranges({{64, 512}, {32, 128}, {2, 128}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_BatchNorm_fp16)
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// ->RangeMultiplier(2)
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->Ranges({{2, 64}, {2, 32}, {2, 256}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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//------------------------------------------------------------------------------
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BENCHMARK(Baseline_BatchNorm_cuDNN_fp32)
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// ->RangeMultiplier(2)
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// cuDNN didn't make it to 1024
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->Ranges({{64, 512}, {32, 128}, {2, 64}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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BENCHMARK(Baseline_BatchNorm_cuDNN_fp32)
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// ->RangeMultiplier(2)
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->Ranges({{2, 64}, {2, 32}, {2, 256}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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BENCHMARK(Baseline_BatchNorm_cuDNN_fp16)
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// ->RangeMultiplier(2)
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->Ranges({{64, 512}, {32, 128}, {2, 128}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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BENCHMARK(Baseline_BatchNorm_cuDNN_fp16)
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// ->RangeMultiplier(2)
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->Ranges({{2, 64}, {2, 32}, {2, 256}})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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//------------------------------------------------------------------------------
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// RESNET and REXNEXT benchmarks
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NVFUSER_BENCHMARK_DEFINE(
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NvFuserScheduler_ResNet_BatchNorm_fp16,
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setupBatchNorm,
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NvFuserScheduler_BatchNorm,
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DataType::Half);
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_ResNet_BatchNorm_fp16)
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->Args({256, 64, 112})
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->Args({256, 64, 56})
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->Args({256, 256, 56})
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->Args({256, 128, 56})
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->Args({256, 128, 28})
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->Args({256, 512, 28})
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->Args({256, 256, 28})
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->Args({256, 256, 14})
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->Args({256, 1024, 14})
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->Args({256, 512, 14})
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->Args({256, 512, 7})
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->Args({256, 2048, 7})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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NVFUSER_BENCHMARK_DEFINE(
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NvFuserScheduler_ResNext_BatchNorm_fp16,
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setupBatchNorm,
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NvFuserScheduler_BatchNorm,
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DataType::Half);
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NVFUSER_BENCHMARK_RUN(NvFuserScheduler_ResNext_BatchNorm_fp16)
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->Args({128, 64, 112})
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->Args({128, 128, 56})
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->Args({128, 256, 56})
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->Args({128, 128, 56})
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->Args({128, 256, 28})
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->Args({128, 512, 28})
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->Args({128, 512, 14})
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->Args({128, 1024, 14})
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->Args({128, 1024, 7})
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->Args({128, 2048, 7})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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//------------------------------------------------------------------------------
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BENCHMARK(Baseline_ResNet_BatchNorm_cuDNN_fp16)
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->Args({256, 64, 112})
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->Args({256, 64, 56})
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->Args({256, 256, 56})
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->Args({256, 128, 56})
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->Args({256, 128, 28})
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->Args({256, 512, 28})
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->Args({256, 256, 28})
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->Args({256, 256, 14})
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->Args({256, 1024, 14})
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->Args({256, 512, 14})
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->Args({256, 512, 7})
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->Args({256, 2048, 7})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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BENCHMARK(Baseline_ResNext_BatchNorm_cuDNN_fp16)
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->Args({128, 64, 112})
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->Args({128, 128, 56})
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->Args({128, 256, 56})
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->Args({128, 128, 56})
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->Args({128, 256, 28})
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->Args({128, 512, 28})
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->Args({128, 512, 14})
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->Args({128, 1024, 14})
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->Args({128, 1024, 7})
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->Args({128, 2048, 7})
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->Unit(benchmark::kMicrosecond)
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->UseManualTime();
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